Video Classification
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Video Classification is the task of producing a label that is relevant to the video given its frames. A good video level classifier is one that not only provides accurate frame labels, but also best describes the entire video given the features and the annotations of the various frames in the video. For example, a video might contain a tree in some frame, but the label that is central to the video might be something else (e.g., “hiking”). The granularity of the labels that are needed to describe the frames and the video depends on the task. Typical tasks include assigning one or more global labels to the video, and assigning one or more labels for each frame inside the video.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Learning Spatio-Temporal Representation with Local and Global Diffusion
Learning Video Representations using Contrastive Bidirectional Transformer
FASTER Recurrent Networks for Efficient Video Classification
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
EEG-based Emotional Video Classification via Learning Connectivity Structure
Hallucinating Optical Flow Features for Video Classification
Exploring Temporal Information for Improved Video Understanding
VideoGraph: Recognizing Minutes-Long Human Activities in Videos
On Flow Profile Image for Video Representation
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
Billion-scale semi-supervised learning for image classification
Factor Analysis in Fault Diagnostics Using Random Forest
DynamoNet: Dynamic Action and Motion Network
Large Scale Holistic Video Understanding
Multi-Branch Tensor Network Structure for Tensor-Train Discriminant Analysis
Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics
Video Classification with Channel-Separated Convolutional Networks
Robust Real-Time Violence Detection in Video Using CNN And LSTM
Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification
Video-based surgical skill assessment using 3D convolutional neural networks
Efficient Video Classification Using Fewer Frames
Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions
Understanding and Training Deep Diagonal Circulant Neural Networks
Adversarial Framing for Image and Video Classification
Timeception for Complex Action Recognition
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language
Deep Multimodal Learning: An Effective Method for Video Classification
Unsupervised Meta-Learning For Few-Shot Image Classification
Deep RNN Framework for Visual Sequential Applications
Multi-Task Learning of Generalizable Representations for Video Action Recognition
Higher-order Network for Action Recognition
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification
Compact Generalized Non-local Network
Cascaded Pyramid Mining Network for Weakly Supervised Temporal Action Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.